SAS® Business Analytics

SAS® Business Analytics Features

Data access

Provides access to data from more than 60 data sources, including relational and nonrelational databases, PC files, Hadoop, Amazon Redshift and data warehouse appliances with a single SAS Business Analytics license.

Data provisioning

Parallel load data from desired data sources into memory simply by selecting them – no need to write code or have experience with an ETL tool. (Data cannot be sent back to the following data sources: Twitter, YouTube, Facebook, Google Analytics, Esri; it can only be sourced form these sites).

Reduce the amount of data being copied by performing row filtering or column filtering before the data is provisioned.

Retain big data in situ, and push processing to the source system by including SAS In-Database optional add-ons.

Guided, interactive data preparation

Transform, blend, shape, cleanse and standardize data in an interactive, visual environment that guides you through data preparation processes.

Easily understand how a transformation affected results, getting visual feedback in near-real-time through the distributed, in-memory processing of SAS Viya.

Machine learning & AI suggestions

Take advantage of AI and machine learning to scan data and make intelligent transformation suggestions.

Accept suggestions and complete transformations at the click of a button. No advanced or complex coding required.

Automated suggestions include:

Casing.

Gender analysis.

Match code.

Parse.

Standardization.

Missing value imputation for numeric variables.

One hot encoding.

Remove column.

Whitespace trimming.

Convert column data type.

Center and scale.

Dedupe.

Unique ID creation.

Column removal for sparse data.

Column-based transformations

Use column-based transformations to standardize, remediate and shape data without doing configurations. You can:

Change case.

Convert column.

Rename.

Remove.

Split.

Trim whitespace.

Custom calculation.

Support for wide tables allows for the saving of data plans for quick data preparation jobs.

Row-based transformations

Use row-based transformations to filter and shape data.

Create analytical-based tables using the transpose transformation to prepare the data for analytics and reporting tasks.

Data profiling

Use the table-level profile metrics to uncover data quality issues and get further insight into the data itself.

Drill into each column for column-level profile metrics and to see visual graphs of pattern distribution and frequency distribution results that help uncover hidden insights.

Use a variety of data types/sources (listed previously). To profile data from Twitter, Facebook, Google Analytics or YouTube, you must first explicitly import the data into the SAS Viya in-memory environment.

Data quality processing

(SAS® Data Quality in SAS® Viya® is included in SAS Data Preparation)

Data cleansing

Use locale- and context-specific parsing and field extraction definitions to reshape data and uncover additional insights.

Batch text analysis

Cloud data exchange

Securely copy data from on-site repositories to a cloud-based SAS Viya instance running in a private or public cloud for use in SAS Viya applications – as well as sending data back to on-site locations.

Preprocess data locally, which reduces the amount of data that needs to be moved to remote locations.

Use a Command Line Input (CLI) interface for administration and control.

Securely and responsibly negotiates your on-site firewall.

Visual data exploration & insights deployment

Provides an integrated environment for self-service data discovery, reporting and world class analytics.

Custom sort allows you to rank order category data items in a table or graph by characteristics (e.g., products, customers). The characteristics that are most important to your organization will be displayed first.

One-click filtering (e.g., one way, bidirectional) and linked selections will allow you to spend less time manually linking content (e.g., visualizations, reports).

Text objects include date-driven or system-generated text for relevant context.

Synchronize selection and filters across visualizations in a report or dashboard.

Link different reports (e.g., link a sales report to an inventory report).

Report consumers can change calculation parameters and display rules using controls, filters, etc. to see information that is most relevant to them.

Report consumers can switch measures and change chart type and formatting all on the fly allowing them to make critical business decisions instantly.

Manage and secure your mobile app and data by integrating with mobile device management (MDM) service (via new APIs).

Embed full reports or individual objects in websites and web apps using the SAS Visual Analytics SDK:

Combine insights from multiple reports in one location.

User selections within an embedded SAS Visual Analytics object can drive other elements anywhere on the webpage.

Location analytics

Geographical maps are enabled through Esri ArcGIS Online or OpenStreetMap.

You can lasso data points on geographical maps to select specific data for further analysis.

Geographical maps make it easy to visualize measurement variances over a geographical area.

Access to all Esri basemaps and geosearch is available through Esri ArcGIS Online at no additional charge.

Custom polygons (e.g., sales territories, voting districts, floor plans, seating charts) will let you see the world just as your business demands for it. These polygons can be animated to show how key metrics change over time.

Geographic point clustering makes it easier to visualize high-volume location data and identify areas of interest. Get more or less details at different zoom levels.

SAS® Viya® in-memory engine

User requests (expressed in a procedural language) are translated into actions with the parameters needed to process in a distributed environment. The result set and messages are passed back to the procedure for further action by the user.

Data is managed in blocks and can be loaded in memory and on demand.

If tables exceed memory capacity, the server caches the blocks on disk. Data and intermediate results are held in memory as long as required, across jobs and users.

Includes highly efficient node-to-node communication. An algorithm determines the optimal number of nodes for a given job.

Communication layer supports fault tolerance and lets you remove or add nodes from a server while it is running. All components can be replicated for high availability.

Support for legacy SAS code and direct interoperability with SAS 9.4M5 clients.

Supports multitenancy deployment, allowing for a shared software stack to support isolated tenants in a secure manner.

Deployment flexibility

On-site deployments:

Single-machine server to support the needs of small to midsized organizations.